Imbalanced learning is concerned with techniques that deal with differences in the number of training examples, by class. When the number of training examples are skewed toward one or a few classes, machine learning models struggle to differentiate minority classes and tend to treat them as "noise" with respect to majority classes. Many types of class imbalance are possible: exponential and step are some of the more common. In this paper, we study exponential imbalance, which is most often found in real-world image data [1]}, [2]}, [3]}.
